Incorporating synoptic-scale climate signals for streamflow modelling over the Mediterranean region using machine learning models-Reference-Cited by-同舟云学术

Incorporating synoptic-scale climate signals for streamflow modelling over the Mediterranean region using machine learning models

Published:2019-07-04 Issue:10 Volume:64 Page:1240-1252
ISSN:0262-6667
Container-title:Hydrological Sciences Journal
language:en
Short-container-title:Hydrological Sciences Journal

Author:

Kisi Ozgur¹,Choubin Bahram²,Deo Ravinesh C.³,Yaseen Zaher Mundher⁴^ORCID

Affiliation:

1. Faculty of Natural Sciences and Engineering, Ilia State University, Tbilisi, Georgia

2. Department of Watershed Management, Sari University of Agricultural Sciences and Natural Resources, Sari, Iran

3. School of Agricultural Computational and Environmental Sciences, Centre of Sustainable Agricultural Catchments & Centre for Applied Climate Sciences, Institute of Life Sciences and the Environment, University of Southern Queensland, Springfield, Australia

4. Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

Publisher

Informa UK Limited

Subject

Water Science and Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/02626667.2019.1632460

Reference72 articles.

1. Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada

2. Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia

3. Assessment of climate change impact on water resources in the Pungwe river basin

4. Climate change and global water resources

5. Criteria for Evaluation of Watershed Models

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